
For our simulations, we deploy \(K=50\)  users uniformly in a square area of size 500 m \(\times \)  500 m with the BS located at its center.
The path loss model is \(128.1+37.6\log _{10} d\)  (\(d\)  is in km)
and the standard deviation of shadow fading is 8 dB [1]}.
In addition, the noise power spectral density is \(N_0=-174\)  dBm/Hz.
We use the real open blog feedback dataset in [2]}.
This dataset with a total number of 60,021 data samples originates from blog posts and the dimensional of each data sample is 281.
The prediction task associated with the data is the prediction
of the number of comments in the upcoming 24 hours.
Parameter \(C_k\)  is uniformly distributed in \([1,3]\times 10^4\)  cycles/sample.
The effective switched capacitance in local computation is \(\kappa =10^{-28}\) .
In Algorithm 1, we set \(\xi =1/10\) , \(\delta =1/10\) , and \(\epsilon _0=10^{-3}\) . Unless specified otherwise, we choose an equal maximum average transmit power \(p_1^{\max }=\cdots =p_K^{\max }=p^{\max }=10\)  dBm, an equal maximum computation capacity \(f_1^{\max }=\cdots =f_K^{\max }=f^{\max }=2\)  GHz, a transmit data size \(s=28.1\)  kbits,
and a bandwidth \(B=20\)  MHz.
Each user has \(D_k=500\)  data samples, which are randomly selected from the dataset with equal probability.
All statistical results are averaged over 1000 independent runs.
<FIGURE>